KeyDNN

KeyDNN is a lightweight deep learning framework built from scratch in Python, with a strong focus on:

• clean architecture and explicit interfaces
• a practical CPU / CUDA execution stack
• correctness-first design validated by CPU ↔ CUDA parity tests

It is designed to be both:

• a learning-friendly implementation of modern DL abstractions (Tensor, autograd, modules), and
• a performance-oriented sandbox for building real backends (native CPU kernels, CUDA kernels, vendor libraries).

🚧 Status: v2.1.0.
The v2 public API is largely stable and continues to evolve incrementally within the v2.x line. Breaking changes are avoided when possible and documented when necessary.
📚 Documentation: https://keywind127.github.io/keydnn_v2/
💻 Source: https://github.com/keywind127/keydnn_v2

Platform support

• OS: Windows 10 / 11 (x64 only)
• Python: ≥ 3.10
• CUDA: Optional (NVIDIA GPU required for acceleration)

CUDA acceleration requires a compatible CUDA runtime. Some backends use vendor libraries such as cuBLAS / cuDNN when available.

If CUDA is unavailable, CPU execution remains supported.

Support snapshot

• Windows (CPU): ✅ supported
• Windows (CUDA): ✅ supported (requires NVIDIA GPU + CUDA runtime; cuBLAS/cuDNN optional)
• Linux/macOS: ❌ not yet supported in v2.x (v0 has CPU-focused Linux support)

Highlights

• CUDA device-pointer–backed Tensor backend
• Explicit H2D / D2H / D2D memory boundaries (no implicit host materialization)
• Vendor-accelerated kernels:
  • cuBLAS GEMM for matmul
  • cuDNN acceleration for conv2d / conv2d_transpose (when enabled)
• CUDA implementations for core ops:
  • elementwise ops
  • reductions
  • pooling
  • in-place scalar ops (optimizer hot paths)
• Extensive CPU ↔ CUDA parity tests
• Standalone microbenchmarks under scripts/

Installation

pip install keydnn

Development install:

git clone https://github.com/keywind127/keydnn_v2.git
cd keydnn_v2
pip install -e .

Quickstart

from keydnn.tensors import Tensor, Device

x = Tensor(shape=(2, 3), device=Device("cpu"), requires_grad=True)
y = (x * 2.0).sum()
y.backward()

print(x.grad.to_numpy())

CUDA example:

from keydnn.tensors import Tensor, Device
from keydnn.backend import cuda_available

device = Device("cuda:0") if cuda_available() else Device("cpu")

x = Tensor.rand((1024, 1024), device=device, requires_grad=True)
y = (x @ x.T).mean()
y.backward()

print("device:", device)
print("y:", y.item())

CUDA setup (Windows)

CUDA requires additional setup on Windows (CUDA runtime discovery and optional cuDNN). See the documentation for details:

• https://keywind127.github.io/keydnn_v2/getting-started/cuda/

Versioning note

KeyDNN v2 is a major rewrite and is not API-compatible with KeyDNN v0.

License

Licensed under the Apache License, Version 2.0.